Artificial Intelligence Jobs in Santa Clara, CA

As a Principal Software Engineer, you will lead AI networking system transformations, manage complex customer engagements, and influence NVIDIA's networking technologies.

Create developer-focused technical content, demos, reference examples, deployment guides, and docs-as-code workflows to accelerate adoption of NVIDIA enterprise AI software. Work across engineering, product, field, and partners to design developer journeys, build sample applications and notebooks, enable sales and partners, capture feedback, and engage the open source and cloud-native communities.

Serve as the embedded systems expert for NVIDIA Physical AI partners, integrating sensors, deploying and optimizing robotics foundation models on Jetson-class edge hardware, guiding customers from prototype to production, and delivering reference implementations and workshops.

Lead developer marketing programs to acquire, activate, and grow developers building AI applications on PC using NVIDIA SDKs and RTX. Create technical content, run community and partner activations, educate developers on platform capabilities, drive engagement and evangelism across channels, and use analytics to improve program performance.

Design and build AI-native, high-performance CI/build/test/validation infrastructure for large Tegra C/C++ codebases. Optimize build graphs, caching, incremental builds, remote execution, device-lab testing, and integrate reasoning agents for autonomous triage, remediation, and self-healing workflows. Package reusable platform capabilities, drive adoption, and measure developer feedback loop improvements.

Lead product strategy and execution for AI-driven Copilot, agentic AI, and workflow orchestration on the Pure1 platform. Define roadmap, drive integrations, expand SaaS subscriptions, and partner with engineering, GTM, and customers to deliver automation and AI-powered infrastructure services.

This role involves designing and exporting AI frameworks, enhancing product capabilities, collaborating with teams, and directly engaging with customers.

Responsible for defining and validating SoC IP blocks, collaborating with teams to ensure quality silicon for AI applications, and mentoring junior engineers.

Build and test Marvis Minis across embedded firmware and cloud services. Develop Minis features, author network/application validation tests, debug cross-layer issues spanning AP firmware to cloud data pipelines, extend platform to additional devices, and maintain cloud processing components (Storm, Airflow, Kafka, Redis, Elasticsearch). Collaborate with firmware, data science, QA, and operations and participate in production rollouts and incident response.

Lead strategy, roadmap, and delivery for HPE's enterprise AI software platform across deployment models. Drive modular architecture (inference, agent deployment, GenAI workflows, edge AI), define GTM and monetization, align cross-functional teams, and deliver executive-ready materials to bring AI products from early stages to revenue.

Build and scale the infrastructure layer for visual intelligence: optimize VLM inference and GPU serving, design multimodal APIs and structured outputs, ensure scalable backend systems, reliability, observability, CI/CD, and developer experience. Drive 0→1 product work with strong testing discipline, schema validation, and performance optimization.

As a Senior Staff Software Engineer, you will develop and maintain AI deployment software, optimizing hardware-software co-design, and work closely with various experts to enhance infrastructure.

The Senior Technical Program Manager is responsible for site and data center operations, leading cross-functional programs, ensuring operational readiness, and managing metrics and KPIs for AI infrastructure.

Lead GPU and HPC cluster architecture and deployment for cloud partners: design end-to-end GPU/NVLink infrastructures, perform performance modeling, validate and debug cluster configurations, support NPI deployments, and serve as technical liaison between engineering, product, and field teams.

Build and optimize local LLM inference and agent runtimes for Windows and GeForce RTX GPUs. Profile and reduce latency/memory, integrate CUDA/TensorRT pipelines, implement sandboxed security and privacy routers, and harden multi-agent orchestration for constrained consumer hardware while collaborating across research, driver, and open-source teams.

The Marketing AI Engineer enhances marketing with AI tools, manages application integrations, tracks AI utilization goals, and fosters team enablement in a 4-month contract role.

Develop and integrate AI agents and ML models to optimize VLSI/chip design flows. Design algorithms, build software tools and frameworks, analyze data to inform agent-driven methodologies, and collaborate with VLSI, ML, and software teams to improve productivity and quality.

Design and validate scalable on‑prem datacenter and cluster reference architectures for enterprise AI systems. Lead multidisciplinary projects across networking, compute, storage, and cloud-native software to produce secure, resilient, and cost‑optimized hybrid AI solutions.

Own global channel strategy and execution for NVIDIA Jetson and IGX platforms. Manage distributor relationships, drive partner onboarding, pipeline creation, revenue, and partner readiness. Collaborate with marketing on channel programs, MDF-funded activities, demand generation, and performance measurement through reviews, scorecards, and dashboards.

Lead research on generative world models and synthetic data to improve training, evaluation, and generalization of end-to-end autonomous driving systems. Prototype and develop algorithms, collaborate with research and product teams to scale advances into production, define research direction and roadmaps, and drive technology transfer for safe Level 4 autonomy.

The role involves integrating AI supercomputing systems into datacenters, collaborating with engineering teams, and producing documentation for processes related to datacenter integration and fit-out.

Create developer-facing technical content for NVIDIA's AI platform: test and evaluate training/inference features, write blogs and guides, build code samples and demos, benchmark multi-GPU systems, engage the developer community, and feed product feedback to engineering and product teams.

Design, implement, and optimize AI algorithms and software for computer vision, NLP, recommendation systems and ASR. Develop, test, and maintain models and production-quality code, participate in code reviews and CI/CD workflows, and document algorithms and technical processes.

Design, build, and deploy reliable, outcome-driven AI systems and agentic workflows that plan, reason, and act across enterprise systems. Integrate LLMs into production, work with structured and unstructured data to ground models in business context, and partner with product to ship end-to-end capabilities.

Build and operate secure, scalable ML pipelines and hybrid GPU infrastructure for training, fine-tuning, serving, and governing open-source LLMs and agentic apps. Implement CI/CD, model versioning, observability, benchmarking, security controls, and integration with RAG/agent tools and vector DBs to support production deployments across tenants.